Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Masking and Demasking Agents01:19

Masking and Demasking Agents

2.4K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

447
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
447
Neural Circuits01:25

Neural Circuits

1.2K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.2K
Modeling and Similitude01:12

Modeling and Similitude

267
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
267
Morphogenesis02:19

Morphogenesis

28.2K
Plant morphogenesis—the development of a plant’s form and structure—involves several overlapping developmental processes, including growth and cell differentiation. Precursor cells differentiate into specific cell types, which are organized into the tissues and organ systems that make up the functional plant.
28.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Identifying Patients at Risk of Early Lethal Prostate Cancer by Integrating Family History, Polygenic Risk Score, Rare Variants in DNA Repair Genes, and Lifestyle Factors.

European urology oncology·2025
Same author

Neural markers of methylphenidate response in children with attention deficit hyperactivity disorder and the impact on executive function.

Frontiers in psychiatry·2025
Same author

Analysis of clinical characteristics and prognosis of childhood functional neurological disorder: Identifying key factors of prognosis and optimizing clinical management.

Journal of psychosomatic research·2025
Same author

Synergistic adsorption mechanism of heavy metals on zinc oxide/phosphate modified gel-biochar composites.

Analytical methods : advancing methods and applications·2025
Same author

Progressively reduced cerebral oxygen metabolism and elevated plasma NfL levels in the zQ175DN mouse model of Huntington's disease.

bioRxiv : the preprint server for biology·2025
Same author

A niche driven mechanism determines response and a mutation-independent therapeutic approach for myeloid malignancies.

Cancer cell·2025

Related Experiment Video

Updated: Jul 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and

Lifei Wang1, Rui Nie2,3,4, Xuexia Miao2,3

  • 1Shulan (Hangzhou) Hospital, Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China. wanglf1020@gmail.com.

BMC Bioinformatics
|January 24, 2024
PubMed
Summary

inClust+ is a new deep generative framework for multimodal single-cell data integration. This computational method enhances multi-omics data analysis by incorporating mask modules for improved data processing and imputation.

Keywords:
Cross-modal generationCross-modal imputationData integrationDeep generative framework with mask modulesMulti-omics

More Related Videos

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

Related Experiment Videos

Last Updated: Jul 5, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.1K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell technologies enable measurement of numerous cell traits.
  • Multi-omics profiling allows simultaneous measurement of multiple traits per cell.
  • Advanced computational methods are crucial for integrating rapidly accumulating multimodal single-cell data.

Purpose of the Study:

  • To introduce inClust+, a deep generative framework for multimodal single-cell data integration.
  • To enhance existing computational methods for processing and analyzing multi-omics data.
  • To address the need for robust tools in the rapidly growing field of single-cell multi-omics.

Main Methods:

  • inClust+ is a deep generative framework built upon the inClust model.
  • It incorporates novel input-mask and output-mask modules for multimodal data processing.
  • The framework utilizes an encoder-decoder architecture enhanced with masking strategies.

Main Results:

  • inClust+ successfully integrated scRNA-seq and MERFISH data, and imputed MERFISH data from scRNA-seq data.
  • The framework demonstrated capability in integrating tri-modal data (gene expression, chromatin accessibility, protein abundance) with batch effects.
  • inClust+ effectively transferred labels and generated missing protein abundance data in multimodal datasets.

Conclusions:

  • inClust+ provides a suitable framework for handling complex multimodal single-cell data.
  • The implemented masking strategy in inClust+ can be adapted to other deep learning models with encoder-decoder architectures.
  • This advancement broadens the application scope of deep learning in multi-omics data analysis.